Coder-decoder-based deep learning multi-step irradiance prediction method

Abstract

The invention relates to a deep learning multi-step irradiance prediction method based on a coder-decoder, and belongs to the technical field of photovoltaic power generation. The prediction method comprises the following steps: S1, training data acquisition: acquiring historical irradiance data of a target area and corresponding meteorological data, and making a supervision data set; s2, data preprocessing, including meteorological information feature coding and data normalization; s3, a coder and decoder model is trained, a coder model is composed of a TCN and LSTM cascade structure, and a decoder is composed of an LSTM and MLP cascade structure; the method comprises the following steps: training a coder-decoder model by using read irradiance of a current time period t0-tN as supervision information and historical irradiance and meteorological information before a t0 moment as input data; and S4, prediction: inputting historical data into the coder-decoder model obtained by training in the step S3, and predicting the solar irradiance of multiple steps in the future. According to the method, the historical information of the irradiance sequence can be fully utilized, and experiments show that the method can effectively improve the precision of multi-step irradiance prediction.

Description

technical field [0001] The invention relates to a deep learning multi-step irradiance prediction method based on a codec, belonging to the technical field of photovoltaic power generation. Background technique [0002] Solar energy is the most promising renewable energy source. According to a survey by the International Renewable Energy Agency, as of 2020, 29% of global electricity production comes from renewable energy sources, of which solar energy accounts for 26.77% of renewable energy generation, and Increased year after year. However, due to the uncertainty and intermittency of irradiance, photovoltaic power generation is quite unstable, which increases the difficulty of grid connection and scheduling of photovoltaic power generation, and restricts the wide application of solar energy resources. [0003] At present, there are many irradiance prediction methods based on deep learning, such as irradiance prediction using models such as LSTM and CNN. Some scholars have ...

AI Technical Summary

Problems solved by technology

However, the CNN model is better at extracting spatial features and has limited ability to extract time-dependent features; the RNN model can maintain timing dependencies, but it is difficult to handle long input sequences

The above characteristics indicate that the deep learning model based on RNN and CNN is difficult to take into account long-term sequence input and long-term dependence, and the model needs to be improved

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